SlamData Makes the List: 2017 DBTA Trend-Setting Products
To help shed light on where customers can go to address their data-driven challenges, Database Trends and Applications magazine assembles an annual list of solutions that can help organizations derive greater benefit from their data, make decisions faster, and work smarter and more securely.
We’re excited to announce that we’ve been included in the 2017 list!
Latest posts by Chris Dima (see all)
- SlamData Makes the List: 2017 DBTA Trend-Setting Products - December 14, 2016
- 2016: The Year Gartner’s Enterprise BI Magic Quadrant Became A Ghost Town - November 9, 2016
- In the BOSSIEs (Again!) – Listed In InfoWorld’s ‘Best of’ Open Source Big Data Tools for 2016 - September 21, 2016
Data Intelligence On Complex, Modern Data. Without Moving Any Of It.
Recent News & Blogs
The following is an interview I conducted with Jeff Carr, CEO and Founder of SlamData regarding the trends in enterprise business intelligence.read more
Enterprise Business Intelligence solutions are failing, and the reasons are very obvious. Leading analyst firms including Gartner and G2 have published rankings which show virtually no leadership and scant challengers in the market.read more
Boulder, CO — SlamData Inc., the company building the industry’s first comprehensive Business Intelligence solution for complex modern data, today announced the release of SlamData 4.0, which marks the debut of new connectors for modern data sources. SlamData 4.0 now supports MongoDB, Apache Spark on Hadoop, MarkLogic and Couchbase.read more
We’re mighty stoked to make it into InfoWorld’s list of best open source big data tools for the second time!read more
Who Is Using SlamData?
Whitepaper: The Characteristics of NoSQL Analytics Systems
by John De Goes, CTO and Co-Founder of SlamData
Semistructured data, called NoSQL data in this paper, is growing at an unprecedented rate. This growth is fueled, in part, by the proliferation of web and mobile applications, APIs, event-oriented data, sensor data, machine learning, and the Internet of Things, all of which are disproportionately powered by NoSQL technologies and data models.
This paper carves out a single concern, by focusing on the system-level capabilities required to derive maximum analytic value from a generalized model of NoSQL data. This approach leads to eight well-defined, objective characteristics, which collectively form a precise capabilities-based definition of a NoSQL analytics system.
These capabilities are inextricably motivated by use cases, but other considerations are explicitly ignored. They are ignored not because they are unimportant (quite the contrary), but because they are orthogonal to the raw capabilities a system must possess to be capable of deriving analytic value from NoSQL data.
Table of Contents
- The Nature of NoSQL Data
- NoSQL Databases
- Big Data
- A Generic Data Model for NoSQL
- Approaches to NoSQL Analytics
- Coding & ETL
- Real-Time Analytics
- Relational Model Virtualization
- First-Class NoSQL Analytics
- Characteristics of NoSQL Analytics Systems
- Generic Data Model
- Isomorphic Data Model
- Unified Schema/Data
- Polymorphic Queries
- Dynamic Type Discovery & Conversion
- Structural Patterns